Data visualization: From body sensor network to social networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sensors can capture very sensitive and valuable information without human intervention and send it to remote location. However, capturing sensory data from a body sensor network (BSN) and sending it to social networks is a challenging task. This is because it requires a number of distributed networks to work together seamlessly. The task becomes more challenging when both the BSN and the social networks are mobile. It requires a framework which can handle the mobility of both the BSN and members of social networks and can send the sensory data to the social networks for real-time visualization. In this paper, we propose an open source framework, named SenseFace, which seamlessly incorporates a four-tier network including a BSN, cellular network, Internet and an overlay network consisting of social networks, to pass sensory data from a mobile BSN to the overlay network. The overlay network can intelligently manage one's social network and produce different data visualization formats suitable for email, fax, voicemail, SMS, MMS, APRS network, IM networks such as hotmail, gmail, yahoo, and existing social networks such as Facebook, YouTube, LinkedIn, delicious, Wordpress etc. Finally, we present the framework design and the hardware and software that have been used for the implementation of the framework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it